Instructions to use wsagi/SmolVLA-PickOrange with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use wsagi/SmolVLA-PickOrange with LeRobot:
# See https://github.com/huggingface/lerobot?tab=readme-ov-file#installation for more details git clone https://github.com/huggingface/lerobot.git cd lerobot pip install -e .[smolvla]
# Launch finetuning on your dataset python lerobot/scripts/train.py \ --policy.path=wsagi/SmolVLA-PickOrange \ --dataset.repo_id=lerobot/svla_so101_pickplace \ --batch_size=64 \ --steps=20000 \ --output_dir=outputs/train/my_smolvla \ --job_name=my_smolvla_training \ --policy.device=cuda \ --wandb.enable=true
# Run the policy using the record function python -m lerobot.record \ --robot.type=so101_follower \ --robot.port=/dev/ttyACM0 \ # <- Use your port --robot.id=my_blue_follower_arm \ # <- Use your robot id --robot.cameras="{ front: {type: opencv, index_or_path: 8, width: 640, height: 480, fps: 30}}" \ # <- Use your cameras --dataset.single_task="Grasp a lego block and put it in the bin." \ # <- Use the same task description you used in your dataset recording --dataset.repo_id=HF_USER/dataset_name \ # <- This will be the dataset name on HF Hub --dataset.episode_time_s=50 \ --dataset.num_episodes=10 \ --policy.path=wsagi/SmolVLA-PickOrange - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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base_model: lerobot/smolvla_base
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---
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#
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针对 [LeIsaac SO-101 PickOrange](https://github.com/LightwheelAI/leisaac) 任务 LoRA-free 微调的 [SmolVLA](https://huggingface.co/lerobot/smolvla_base) 策略 — 自训 30k step。
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_A fine-tuned [SmolVLA](https://huggingface.co/lerobot/smolvla_base) policy on the [LeIsaac SO-101 PickOrange](https://github.com/LightwheelAI/leisaac) task, 30k steps full-parameter from `lerobot/smolvla_base`._
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- [vitorcen/isaaclab-experience](https://github.com/vitorcen/isaaclab-experience) — Isaac Lab + LeIsaac 多策略横评(parent project)— 含 7-baseline benchmark
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- [vitorcen/LeIsaac-Training](https://github.com/vitorcen/LeIsaac-Training) — LeIsaac fork(训练脚本 + 设计文档 / training scripts + design docs)
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> **命名注意 / Naming note**:仓库名是 `
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> _Despite the repo name, `config.type=smolvla` (v1). LeRobot's smolvla2 (with LoRA enabled) hadn't merged to main at training time; the "2" is carried over from the local output directory naming._
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## TL;DR
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- 详见 [`vitorcen/isaaclab-experience`](https://github.com/vitorcen/isaaclab-experience) 的 `LeIsaac/README.md` benchmark section
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- **⚠️ 推理 inference 配置**:
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- `policy_action_horizon=50`(= chunk_size,全 chunk receding window)
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- LeRobot async server 端 `--policy_checkpoint_path=wsagi/
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- `step_hz=30` 匹配 dataset
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## 模型亮点
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--policy_type=lerobot-smolvla \
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--policy_host=127.0.0.1 --policy_port=8080 \
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--policy_action_horizon=50 \
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--policy_checkpoint_path=wsagi/
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--policy_language_instruction='Pick up the orange and place it on the plate' \
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--device=cuda --enable_cameras
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```
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```python
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from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy
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policy = SmolVLAPolicy.from_pretrained("wsagi/
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# 见 LeRobot 文档
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```
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base_model: lerobot/smolvla_base
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---
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# SmolVLA-PickOrange
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针对 [LeIsaac SO-101 PickOrange](https://github.com/LightwheelAI/leisaac) 任务 LoRA-free 微调的 [SmolVLA](https://huggingface.co/lerobot/smolvla_base) 策略 — 自训 30k step。
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_A fine-tuned [SmolVLA](https://huggingface.co/lerobot/smolvla_base) policy on the [LeIsaac SO-101 PickOrange](https://github.com/LightwheelAI/leisaac) task, 30k steps full-parameter from `lerobot/smolvla_base`._
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- [vitorcen/isaaclab-experience](https://github.com/vitorcen/isaaclab-experience) — Isaac Lab + LeIsaac 多策略横评(parent project)— 含 7-baseline benchmark
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- [vitorcen/LeIsaac-Training](https://github.com/vitorcen/LeIsaac-Training) — LeIsaac fork(训练脚本 + 设计文档 / training scripts + design docs)
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> **命名注意 / Naming note**:仓库名是 `SmolVLA-PickOrange` 但 `config.type=smolvla`(v1,SmolVLM2-500M-Video-Instruct backbone + Action Expert)。LeRobot 当时没把 `smolvla2`(带 LoRA on)merge 到 main,所以这里仍是 v1。命名是 dir 命名误称延续。
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> _Despite the repo name, `config.type=smolvla` (v1). LeRobot's smolvla2 (with LoRA enabled) hadn't merged to main at training time; the "2" is carried over from the local output directory naming._
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## TL;DR
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- 详见 [`vitorcen/isaaclab-experience`](https://github.com/vitorcen/isaaclab-experience) 的 `LeIsaac/README.md` benchmark section
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- **⚠️ 推理 inference 配置**:
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- `policy_action_horizon=50`(= chunk_size,全 chunk receding window)
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- LeRobot async server 端 `--policy_checkpoint_path=wsagi/SmolVLA-PickOrange`
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- `step_hz=30` 匹配 dataset
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## 模型亮点
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--policy_type=lerobot-smolvla \
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--policy_host=127.0.0.1 --policy_port=8080 \
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--policy_action_horizon=50 \
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--policy_checkpoint_path=wsagi/SmolVLA-PickOrange \
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--policy_language_instruction='Pick up the orange and place it on the plate' \
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--device=cuda --enable_cameras
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```
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```python
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from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy
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policy = SmolVLAPolicy.from_pretrained("wsagi/SmolVLA-PickOrange")
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# 见 LeRobot 文档
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```
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